class: inverse, middle, center
Intro to Data
How is data stored, how do we use it?
- Often, data is in an excel sheet, or a plain text file (.csv, .txt)
- .csv files open in Excel automatically, but actually are plain text
- Usually, columns are variables/measures and rows are observations (i.e. a person’s measurements)
Our example data:
Download data csv file link and pay attention to where it downloads on your computer
- Make sure it is a .csv file and not a “web archive” or something else.
Open the data file penguins.csv and look at it
- What are the columns? What are the rows?
About the penguins data
- A data set about penguins at Palmer Station, Antarctica! More info at github.com/allisonhorst/palmerpenguins
- Data were collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of the Long Term Ecological Research Network.
- Each row is a penguin measurement
- Some false missingness was induced for this practice.
Workflow - Keep it together!
Steps for a new data analysis project or homework:
- Create a folder to contain all your files.
- Move data file (
penguins.csv) into this folder. - Create an Rstudio project inside this folder. (next slides)
- Create a new Rmd for your analyses/homework.
Do steps 1 & 2 now!
R Projects (.Rproj file) & Good Practices
Use projects to keep everything together (read this) - A project keeps track of your coding environment and file structure. - Create an RStudio project for each data analysis project, for each homework assignment, etc. - A project is associated with a directory folder + Keep data files there + Keep code scripts there; edit them, run them in bits or as a whole + Save your outputs (plots and cleaned data) there - Only use relative paths, never absolute paths + relative (good): read.csv("data/mydata.csv") + absolute (bad): read.csv("/home/yourname/Documents/stuff/mydata.csv")
Advantages of using projects - standardizes file paths - keep everything together - a whole folder can be easily shared and run on another computer - when you open the project everything is as you left it
Create a new R project
Let’s go through it together. (Read this for more)
.pull-left-60[ - Click
in top left or File -> New Project - Click Existing Directory - Browse to your folder with the data - Optional Click “Open in new session checkbox” - Click “Create project”] .pull-right-40[
]
Bonus lessons
The data file will be in your Files pane:
and your workspace folder location will be showing at the top (i.e. Home/Desktop/workshop_practice)
Data in R/Rstudio
Open penguins.csv in Rstudio and look at it
- Click on
penguins.csvin the Files pane, click View File
We will show you how to store and use this data in R as a data frame
Currently it is still just a file in your folder.
To run and save your code: Create a new Rmd!
- Then save it with a meaningful filename.
- You will be prompted to save it in your current working folder.
Load the packages we need in the Rmd
Add this code to the setup chunk in the Rmd and run that chunk:
.pull-left[
library(tidyverse)## ── Attaching packages ────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.1
## ✓ tidyr 1.1.1 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ───────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(janitor)##
## Attaching package: 'janitor'## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test
]
Now we can use functions in these packages, such as read_csv() and %>% and mutate() and tabyl()
Remove everything in the Rmd below this code
- Loading library code should always be at the top of your Rmd so you can use these packages in code “lower down”
Load the data set into R
- Create a new code chunk (Code -> insert chunk)
- Read in csv file from file path with code (filepath relative to Rproj directory)
- Copy this code to that code chunk and run it.
penguins <- read_csv("penguins.csv")## Parsed with column specification:
## cols(
## id = col_double(),
## species = col_character(),
## island = col_character(),
## bill_length_mm = col_double(),
## bill_depth_mm = col_double(),
## flipper_length_mm = col_double(),
## body_mass_g = col_double(),
## sex = col_character(),
## year = col_double()
## )## Parsed with column specification:
## cols(
## id = col_double(),
## age = col_character(),
## sex = col_character(),
## grade = col_character(),
## race4 = col_character(),
## bmi = col_double(),
## weight_kg = col_double(),
## text_while_driving_30d = col_character(),
## smoked_ever = col_character(),
## bullied_past_12mo = col_logical()
## )- Or, open saved file using Import Dataset button in Environment window:
+ From Text(readr).
- If you use this option, then copy and paste the importing code to your Rmd so you have a record of from where and how you loaded the data set.
# Run in console:
View(penguins)
# Can also view the data by clicking on its name in the Environment tab
Your Rmd should look something like this:
Try knitting it!
Object types
Data frames (aka “tibbles” in tidyverse)
.pull-left-60[ Vectors vs. data frames: a data frame is a collection (or array or table) of vectors
penguins## # A tibble: 342 x 9
## id species island bill_length_mm bill_depth_mm flipper_length_…
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1689 Adelie Torge… 39.1 18.7 181
## 2 4274 Adelie Torge… NA 17.4 186
## 3 4539 Adelie Torge… 40.3 18 195
## 4 2435 Adelie Torge… 36.7 19.3 193
## 5 2326 Adelie Torge… 39.3 20.6 190
## 6 2637 Adelie Torge… 38.9 17.8 181
## 7 4443 Adelie Torge… NA 19.6 195
## 8 2102 Adelie Torge… 34.1 18.1 193
## 9 2975 Adelie Torge… 42 20.2 190
## 10 3966 Adelie Torge… 37.8 17.1 186
## # … with 332 more rows, and 3 more variables: body_mass_g <dbl>, sex <chr>,
## # year <dbl>] .pull-right-40[
Allows different columns to be of different data types (i.e. numeric vs. text)
Both numeric and text can be stored within a column (stored together as text).
Vectors and data frames are examples of objects in R.
- There are other types of R objects to store data, such as matrices, lists.
- These will be discussed in future R workshops. ]
Variable (column) types
| type | description |
|---|---|
| double/numeric | numbers that are decimals |
| character | text, “strings” |
| integer | integer-valued numbers |
| factor | categorical variables stored with levels (groups) |
| logical | boolean (TRUE, FALSE) |
- We will focus on double & character, as most data will be of this type when using
read_csv()to read in your data sets - If you see
int= integer as a column type, you can treat it as a double for most intents and purposes.
Data structure
- What are the different variable types in this data set?
- What is
NA?
glimpse(penguins) # structure of data## Rows: 342
## Columns: 9
## $ id <dbl> 1689, 4274, 4539, 2435, 2326, 2637, 4443, 2102, 297…
## $ species <chr> "Adelie", "Adelie", "Adelie", "Adelie", "Adelie", "…
## $ island <chr> "Torgersen", "Torgersen", "Torgersen", "Torgersen",…
## $ bill_length_mm <dbl> 39.1, NA, 40.3, 36.7, 39.3, 38.9, NA, 34.1, 42.0, 3…
## $ bill_depth_mm <dbl> 18.7, 17.4, 18.0, 19.3, 20.6, 17.8, 19.6, 18.1, 20.…
## $ flipper_length_mm <dbl> 181, 186, 195, 193, 190, 181, 195, 193, 190, 186, 1…
## $ body_mass_g <dbl> 3750, 3800, 3250, 3450, 3650, 3625, 4675, 3475, 425…
## $ sex <chr> "male", "female", "female", "female", "male", "fema…
## $ year <dbl> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 200…Data set summary
summary(penguins)## id species island bill_length_mm
## Min. :1001 Length:342 Length:342 Min. :32.10
## 1st Qu.:2031 Class :character Class :character 1st Qu.:39.45
## Median :2984 Mode :character Mode :character Median :44.70
## Mean :3031 Mean :44.00
## 3rd Qu.:4073 3rd Qu.:48.52
## Max. :4969 Max. :59.60
## NA's :6
## bill_depth_mm flipper_length_mm body_mass_g sex
## Min. :13.10 Min. :172.0 Min. :2700 Length:342
## 1st Qu.:15.60 1st Qu.:190.0 1st Qu.:3550 Class :character
## Median :17.30 Median :197.0 Median :4050 Mode :character
## Mean :17.15 Mean :200.9 Mean :4202
## 3rd Qu.:18.70 3rd Qu.:213.0 3rd Qu.:4750
## Max. :21.50 Max. :231.0 Max. :6300
##
## year
## Min. :2007
## 1st Qu.:2007
## Median :2008
## Mean :2008
## 3rd Qu.:2009
## Max. :2009
## Show (print) whole data frame
Tibble truncates the output to ten rows, so you can’t actually see it all.
penguins## # A tibble: 342 x 9
## id species island bill_length_mm bill_depth_mm flipper_length_…
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1689 Adelie Torge… 39.1 18.7 181
## 2 4274 Adelie Torge… NA 17.4 186
## 3 4539 Adelie Torge… 40.3 18 195
## 4 2435 Adelie Torge… 36.7 19.3 193
## 5 2326 Adelie Torge… 39.3 20.6 190
## 6 2637 Adelie Torge… 38.9 17.8 181
## 7 4443 Adelie Torge… NA 19.6 195
## 8 2102 Adelie Torge… 34.1 18.1 193
## 9 2975 Adelie Torge… 42 20.2 190
## 10 3966 Adelie Torge… 37.8 17.1 186
## # … with 332 more rows, and 3 more variables: body_mass_g <dbl>, sex <chr>,
## # year <dbl>View whole data frame
We showed this already, very handy to see all data. Run in console since it’s more interactive.
View(penguins)Data set info
.pull-left-40[
dim(penguins)## [1] 342 9nrow(penguins)## [1] 342ncol(penguins)## [1] 9]
.pull-right-60[
names(penguins)## [1] "id" "species" "island"
## [4] "bill_length_mm" "bill_depth_mm" "flipper_length_mm"
## [7] "body_mass_g" "sex" "year"]
View the beginning of a data set
head(penguins)## # A tibble: 6 x 9
## id species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1689 Adelie Torge… 39.1 18.7 181 3750
## 2 4274 Adelie Torge… NA 17.4 186 3800
## 3 4539 Adelie Torge… 40.3 18 195 3250
## 4 2435 Adelie Torge… 36.7 19.3 193 3450
## 5 2326 Adelie Torge… 39.3 20.6 190 3650
## 6 2637 Adelie Torge… 38.9 17.8 181 3625
## # … with 2 more variables: sex <chr>, year <dbl>View the end of a data set
tail(penguins)## # A tibble: 6 x 9
## id species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1947 Chinst… Dream 45.7 17 195 3650
## 2 4452 Chinst… Dream 55.8 19.8 207 4000
## 3 2420 Chinst… Dream 43.5 18.1 202 3400
## 4 4861 Chinst… Dream 49.6 18.2 193 3775
## 5 4865 Chinst… Dream 50.8 19 210 4100
## 6 4162 Chinst… Dream 50.2 18.7 198 3775
## # … with 2 more variables: sex <chr>, year <dbl>Specify how many rows to view at beginning or end of a data set
head(penguins, 3)## # A tibble: 3 x 9
## id species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1689 Adelie Torge… 39.1 18.7 181 3750
## 2 4274 Adelie Torge… NA 17.4 186 3800
## 3 4539 Adelie Torge… 40.3 18 195 3250
## # … with 2 more variables: sex <chr>, year <dbl>tail(penguins, 1)## # A tibble: 1 x 9
## id species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 4162 Chinst… Dream 50.2 18.7 198 3775
## # … with 2 more variables: sex <chr>, year <dbl>Data frame cells, rows, or columns (rarely used)
.pull-left-60[
Specific cell: DatSetName[row#, column#]
# Second row, Third column
penguins[2, 3]## # A tibble: 1 x 1
## island
## <chr>
## 1 TorgersenEntire row: DatSetName[row#, ]
# Second row
penguins[2,]## # A tibble: 1 x 9
## id species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 4274 Adelie Torge… NA 17.4 186 3800
## # … with 2 more variables: sex <chr>, year <dbl>]
.pull-right-40[ Entire col: DatSetName[, column#]
# Third column
penguins[, 3]## # A tibble: 342 x 1
## island
## <chr>
## 1 Torgersen
## 2 Torgersen
## 3 Torgersen
## 4 Torgersen
## 5 Torgersen
## 6 Torgersen
## 7 Torgersen
## 8 Torgersen
## 9 Torgersen
## 10 Torgersen
## # … with 332 more rows]